def analyze(filename):
pil_img = Image.open(filename)
image = misc.fromimage(pil_img.convert("L"))
isize = interface_size(image)
# transfer distances
# connectivity
td1, connect1, td2, connect2 = transfer_distance(image)
# get the domain size of the white phase
# then invert the image to get the black domain size
image = (image > image.mean())
ads1, std1 = average_domain_size(image)
inverted = (image < image.mean())
ads2, std2 = average_domain_size(inverted)
# bottlenecks of phase1, phase2
b41, b21 = bottleneck_distribution(image)
b42, b22 = bottleneck_distribution(inverted)
# fraction of phase one
phase1 = np.count_nonzero(image)
fraction = float(phase1) / float(image.size)
# Fractal dimension by box counting -- get the slope of the log/log fit
bcd = -1.0 * box_counting_dimension(image)[0]
# count the number of pepper defects
spots = np.logical_xor(
image, ndimage.binary_erosion(image, structure=np.ones((2, 2))))
erosion_spots = np.count_nonzero(spots)
spots = np.logical_xor(
image, ndimage.binary_dilation(image, structure=np.ones((2, 2))))
dilation_spots = np.count_nonzero(spots)
spots = np.logical_xor(
image,
ndimage.binary_closing(
image,
structure=ndimage.morphology.generate_binary_structure(2, 1)))
closure_spots = np.count_nonzero(spots)
blur = modify.ublur_and_threshold(image, 3)
x = np.logical_xor(image, blur)
nzBlur = np.count_nonzero(x)
results = {k: v for k, v in locals().iteritems() if k in keys}
return ' '.join(['{%s:%s}' % (k, f)
for k, f in zip(keys, fmts)]).format(**results)
def analyze(filename):
pil_img = Image.open(filename)
image = misc.fromimage(pil_img.convert("L"))
isize = interface_size(image)
# transfer distances
# connectivity
td1, connect1, td2, connect2 = transfer_distance(image)
# get the domain size of the white phase
# then invert the image to get the black domain size
image = (image > image.mean())
ads1, std1 = average_domain_size(image)
inverted = (image < image.mean())
ads2, std2 = average_domain_size(inverted)
# bottlenecks of phase1, phase2
b41, b21 = bottleneck_distribution(image)
b42, b22 = bottleneck_distribution(inverted)
# fraction of phase one
phase1 = np.count_nonzero(image)
fraction = float(phase1) / float(image.size)
# Fractal dimension by box counting -- get the slope of the log/log fit
bcd = -1.0* box_counting_dimension(image)[0]
# count the number of pepper defects
spots = np.logical_xor( image, ndimage.binary_erosion(image, structure=np.ones((2,2))) )
erosion_spots = np.count_nonzero(spots)
spots = np.logical_xor( image, ndimage.binary_dilation(image, structure=np.ones((2,2))) )
dilation_spots = np.count_nonzero(spots)
spots = np.logical_xor( image, ndimage.binary_closing(image, structure=ndimage.morphology.generate_binary_structure(2, 1)) )
closure_spots = np.count_nonzero(spots)
blur = modify.ublur_and_threshold(image, 3)
x = np.logical_xor(image, blur)
nzBlur = np.count_nonzero(x)
results = {k : v for k, v in locals().iteritems() if k in keys}
return ' '.join(['{%s:%s}' % (k, f) for k, f in zip(keys, fmts)]).format(**results)
if handle not in rescore_list:
rescore_list.append(handle)
# this works around a weird bug in scipy with 1-bit images
pil = Image.open(handle)
image = misc.fromimage(pil.convert("L"))
mutation = random.randrange(7)
debug('mutation={} path={}', mutation, handle)
if mutation == 0:
# gaussian blur
image = modify.gblur_and_threshold(image, random.choice([2,3,4,5]))
elif mutation == 1:
# uniform blur
image = modify.ublur_and_threshold(image, random.choice([2,3,4,5]))
elif mutation == 2:
# invert
inverted = (image < image.mean())
image = inverted
elif mutation == 3:
# add image-wide noise
noise = np.random.random( image.shape )
child = modify.blend_and_threshold(image, noise)
image = child
elif mutation == 4:
# roughen the edges
image = modify.roughen(image)
elif mutation == 5:
# rescale larger
with warnings.catch_warnings(record=True) as warns:
rescore_list.append(handle)
# this works around a weird bug in scipy with 1-bit images
pil = Image.open(handle)
image = misc.fromimage(pil.convert("L"))
mutation = random.randrange(7)
debug('mutation={} path={}', mutation, handle)
if mutation == 0:
# gaussian blur
image = modify.gblur_and_threshold(image,
random.choice([2, 3, 4, 5]))
elif mutation == 1:
# uniform blur
image = modify.ublur_and_threshold(image,
random.choice([2, 3, 4, 5]))
elif mutation == 2:
# invert
inverted = (image < image.mean())
image = inverted
elif mutation == 3:
# add image-wide noise
noise = np.random.random(image.shape)
child = modify.blend_and_threshold(image, noise)
image = child
elif mutation == 4:
# roughen the edges
image = modify.roughen(image)
elif mutation == 5:
# rescale larger
with warnings.catch_warnings(record=True) as warns:
